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Record W2507192164 · doi:10.2118/181543-ms

A Universal Model of Water Flow Through Nanopores in Unconventional Reservoirs: Relationships Between Slip, Wettability and Viscosity

2016· article· en· W2507192164 on OpenAlexafffund
Keliu Wu, Zhangxin Chen, Jinze Xu, Yuan Hu, Jing Li, Xiaohu Dong, Yuxuan Liu, Mingjun Chen

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2016
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCMG Reservoir Simulation Foundation
KeywordsWettingNanoporeSlip (aerodynamics)Materials scienceViscosityMechanicsHagen–Poiseuille equationWater flowFlow (mathematics)Petroleum engineeringNanotechnologyGeotechnical engineeringGeologyComposite materialThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract Understanding and controlling flow of the water confined in nanopores has tremendous implications in theoretical studies and industrial applications. Here we propose a universal model for the confined water flow based on a conception of effective slip, which is linear sum of true slip, only depending on wettability, and apparent slip, caused by the spatial variation of the confined water viscosity as a function of wettability as well as nanopores dimension. Results by the model show that the flow capacity of the confined water is 10−1~107 times of those calculated by no slip Hagen-Poiseuille equation for nanopores with various wettability, in agreement with 47 different cases from the literature. This work may shed light on the controversy over the increase or decrease in flow capacity from the MD simulations and experiments, and guide to tailor the nanopores structure for modulating the confined water flow in many engineering fields, including nanomedicine, water purification, energy storage as well as the flowback of fracture fluid in petroleum industry.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.239
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2016
Admission routes2
Has abstractyes

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